Learning Space-Time Semantic Correspondences

We propose a new task of space-time semantic correspondence prediction in videos. Given a source video, a target video, and a set of space-time key-points in the source video, the task requires predicting a set of keypoints in the target video that are the semantic correspondences of the provided source keypoints. We believe that this task is important for fine-grain video understanding, potentially enabling applications such as activity coaching, sports analysis, robot imitation learning, and more. Our contributions in this paper are: (i) proposing a new task and providing annotations for space-time semantic correspondences on two existing benchmarks: Penn Action and Pouring; and (ii) presenting a comprehensive set of baselines and experiments to gain insights about the new problem. Our main finding is that the space-time semantic correspondence prediction problem is best approached jointly in space and time rather than in their decomposed sub-problems: time alignment and spatial correspondences

Detect-and-Track: Efficient Pose Estimation in Videos

This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection and video understanding. Our method operates in two-stages: keypoint estimation in frames or short clips, followed by lightweight tracking to generate keypoint predictions linked over the entire video. For frame-level pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D extension of this model, which leverages temporal information over small clips to generate more robust frame predictions. We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark, PoseTrack, to validate various design choices of our model. Our approach achieves an accuracy of 55.2% on the validation and 51.8% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack and webpage: https://rohitgirdhar.github.io/DetectAndTrack

MEXSVMs: Mid-level Features for Scalable Action Recognition

This paper introduces MEXSVMs, a mid-level representation enabling efficient recognition of actions in videos. The entries in our descriptor are the outputs of several movement classifiers evaluated over spatial-temporal volumes of the image sequence, using space-time interest points as low-level features. Each movement classifier is a simple exemplar-SVM, i.e., an SVM trained using a single positive video and a large number of negative sequences. Our representation offers two main advantages. First, since our mid-level features are learned from individual video exemplars, they require minimal amount of supervision. Second, we show that even simple linear classification models trained on our global video descriptor yield action recognition accuracy comparable to the state-of-the-art. Because of the simplicity of linear models, our descriptor can efficiently learn classifiers for a large number of different actions and to recognize actions even in large video databases. Experiments on two of the most challenging action recognition benchmarks demonstrate that our approach achieves accuracy similar to the best known methods while performing 70 times faster than the closest competitor